In this work, we address the problem of task-free bottom-up visual attention of predicting human eye fixations in natural images with a CNN architecture.
CNNs are powerful visual models of learning features from data and yield hierarchies of features by building high-level features from low-level ones.
It is also well-known that hierarchical processing is ubiquitous in low-level HVS [1]}.
This makes CNN models a powerful tool for tackling the problem of human eye fixation prediction. For fully exploiting the powerful hierarchical representations of CNNs, a skip architecture is
designed to capture multi-level saliency response, ranging from the local to the global, using
shallow to deep convolutional layers with small to large receptive fields. The proposed CNNs based attention model learns visual attention at
multiple scales and multiple levels in a deep supervision manner [2]}.
As shown in Fig. REF , the final attention prediction is achieved via the deep fusion of various saliency estimations from multiple levels.
Another advantage is that the multi-scale saliency is learned within a single network, which is more succinct compared with conventional multi-scale attention models with multi-stream networks.
